The main transformational forces behind the digital business - from DevOps to IoT and the explosion in hybrid infrastructure complexity - are driving a shift in how we manage the technology of business. With digital transformation swiftly accelerating the pace of business, organizations will need to become even more data-centric in their operations in 2018.
The nature of digitalization today is rooted in more data moving more quickly than ever before. If you ask any security operations team about the complexity of the threats and the type of landscape they face each day, the ever-changing face of this challenge is clear.
To keep up with shifting industry and user demands, businesses must adopt new platforms, shorten development and delivery cycles, move more quickly, and make the most of the opportunities offered by emerging technologies like IoT. Unsurprisingly, managing all of these together is becoming vastly complex.
IT operations and security teams face a major hurdle; how can they enable the efficient use of all the information available without simultaneously opening systems and data up to an unacceptable level of risk?
New challenges require new solutions. IT teams faced with this challenge will require help, and will look to analytic tools, rich data gathering and processing, and machine learning for support. In particular, two key trends – machine learning and predictive analytics – will become mainstream next year as CIOs work to deliver the digital transformation their business requires.
Making the most of machine learning
More raw information is available to businesses as we head towards a fully digital world. Making sense of that data is the key to staying ahead of the competition. Yet there is simply too much information, moving too quickly, for humans to process and understand. We are already data rich and information poor.
Machine learning-based services allow organizations to turn available data into information, and information into actionable intelligence that underpins predictive analytics to operate faster, smarter, and more competitively.
Next year will see organizations embracing the adoption of explicit analytic technologies. These are designed to take in large volumes of operation, configuration, and security data in order to deliver intelligence and insight to those teams entrusted with keeping the lights on – without compromising security.
When teamed up with Big Data, embedding machine learning capabilities in these analytic tools will not only allow for better use of data to answer the teams’ key questions, but will also provide additional insight to ensure the team is asking the right questions.
As 2018 unfolds, the industry should expect to see an increase in the number of business and operational decisions made based on the questions this technology suggested we ask, not just on the answers provided by these enhanced analytic tools.
Tailored micro-analytic capabilities
Micro-analytic capabilities will also become more popular this year. Specifically, this refers to the technologies used to provide use-case specific analytic capabilities designed to work alongside the broader analytic tools described above. These micro-analytic capabilities can be adjusted and tailored for specific uses across technology, security teams and business teams.
By embedding analytics in other tools, organizations can create a broad base of intelligence to build on. This information can then be used to support increasingly stretched operations and security teams as they cope with the massive complexity of all the various moving parts within hybrid infrastructure – both now and as it continues to develop.
Useful and meaningful intelligence
2018 will herald new levels of complexity for those entrusted with managing the digital business or controlling the privacy and security of data. Becoming far more data-centric in business operations is key to successfully tackling these looming challenges.
Machine learning and analytics offer the promise of handling all of the information overwhelming businesses today – and extracting insights that we would never have the time or capacity to see for ourselves. The required levels of data-centricity are only possible now that Big Data stores have been coupled with these capabilities, allowing organizations to successfully separate useable, meaningful intelligence from the “noise” of unstructured – and often poorly organized – raw data.
Fortunately, businesses can make the most of this new set of machine-based allies in 2018, equipping themselves with analytic capabilities adept at solving complex issues now and in coming years.