Companies that want to succeed in digital transformation are investing more than their competitors around data, according to research from PwC want more out of their data – the top five percent of companies are spending around a third more on their initiatives to meet their demands around always-on applications and modern infrastructures.
These investments don’t just commit them to those digital strategies – they produce huge amounts of data from both their internal applications and from external services all day, every day. However, the problem is getting value out of this data, writes Christian Beedgen, CTO and Co-Founder at Sumo Logic.
Delivering this value means looking at the processes that exist across your business as well as capturing that data.
Gartner calls this continuous intelligence (CI), where data becomes an integral element of ongoing business using real-time analytics.
They’ve identified CI as one of the top data and analytics technology trends in 2020 estimating over half of major new business systems will incorporate continuous intelligence and use real-time context data to improve decisions.
However, this statement masks a huge amount of process changes and business operations required to make things work.
Why is Continuous Intelligence Useful Now?
Continuous Intelligence is about making data available to users at the point they need it to influence their decisions. Rather than being simply about analysing data – useful though this functionality may be – CI looks at how to make those results useful where decisions are made.
To achieve CI, businesses need to understand how they obtain data from all their systems, how that data can be analysed more efficiently, and how to make that information available to those who need it in the right context. CI also means closing the loop to provide feedback to the teams involved directly around the decisions they have made. This turning analytics into a business process can deliver more value over time.
One of the biggest drivers of CI today is the availability of data that can be processed, analysed and then used. Applications fulfill more and more business functions, while the data from those applications can show the results of any changes on how customers respond. Rather than indirect signals that don’t clearly correlate with business decisions, any change to a product delivered by an app will have ‘before and after’ data that can show the impact. If a decision is a good one, then more sales or more customer interaction can be seen; if a decision is a bad one, then results will go down.
However, CI is not just as simple as analysing data – instead, it’s about understanding all your data in the right context and using it as part of wider approaches to how to run the organisation.
This approach is part of the wider trend towards using data for building up competitive advantages or to create new products for customers. Rather than concentrating on physical products alone, companies are using a mix of physical and digital services to create their new offerings.
The availability of data, and the speed with which it can be understood, analysed and reframed, helps to reduce the gaps that can exist across a company and then drive success in the Intelligence Economy. When you have real-time responsibilities for how a business is performing over time, you have to get data back for decision-making around issues like development, security and business operations just as quickly.
Alongside this, the role for CI should involve more than one team. Consider a business application as an example – as it works, it should produce a set of data on transactions. This data set can directly help the business observe how many sales were made or orders placed, but it can also provide insight to individuals or teams involved in the applications. For developers, the volume of orders can be compared to the application components and how well they perform to spot the impact of slow service; for security professionals, the application can provide information on how it works for checking against security and compliance frameworks over time.
All this data is the same – however, what it gets used for differs across the business, reducing the intelligence gaps that exist between teams. By sharing this data and using it for specific purposes, processes can get improved. However, there is a substantial side benefit here too – for example, rather than each team storing their own copies of log data for analysis, one set can be used for multiple purposes based on context. For enterprises that create large volumes of data, this represents a significant cost saving through cutting down data silos.
Equally, it can be more efficient to store sets of data in the cloud, so archiving older data for retrieval and analysis when needed can also help reduce the cost. The alternative is to let teams keep their own logs or metrics for shorter periods, which can lead to datasets that are both less rich and also more expensive.
Turning CI into Business Practice
For businesses, the opportunity around CI is how to embed intelligence into working practices. Every team should be able to effectively leverage data in their work – from developers spotting issues in their application performance earlier, to security teams seeing new risks faster, or business teams understanding the impact of their changes for the future.
What makes CI different to real-time analytics alone is how it involves creating a set of business objectives that can be translated into service levels and indicators at the team, function and application levels. This direct correlation between signals from IT infrastructure and organisational performance must be understood.
Rather than one-off reports or even dashboards for analysis, the opportunity here centres on how to make this a natural process over time. It’s only by creating these goals and feedback loops that companies will make the most of their data.