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Forging a competitive advantage in the analytics economy

(Image credit: Image source: Shutterstock/ESB Professional)

Ever since software was first embraced by enterprises, businesses have been collecting data for a wide variety of operational purposes. Only now are many of them starting to realise just how valuable that data can be, particularly relating to business intelligence and informed decision making. But just as this epiphany is taking place, a new problem is rearing its head. Expert manual analysis has long been the main method of extracting actionable intelligence from business data, but as the volume and complexity of data has increased over recent years, analysts simply can’t keep up using this approach anymore. A new method is needed.

Fortunately, the emergence of data-driven technologies such as self-service analytics, machine learning and intelligent automation has marked the start of a new data revolution. Using these tools, businesses can gain fast, accurate insight and improve efficiencies across any number of business processes without having to rely on time consuming manual analysis.  

451 Research has labelled this concept 'Pervasive Intelligence,’ empowering businesses to evolve products and services rapidly to meet the changing needs of their customers. It becomes even more important when many forms of business data have a relatively short shelf life before the intelligence contained within them becomes redundant. In a perfect scenario, the time between collecting data, analysing it and using those insights to make informed decisions would be zero.

While this remains a pipe dream for the majority of businesses out there today, there’s a growing acknowledgment about the importance of machine data analytics tools for key business intelligence functions beyond the IT department. A recent study by 451 Research, in partnership with Sumo Logic, revealed many of today’s leading businesses already see it as the key to delivering an optimal customer experience and driving more intelligent decision making throughout the organisation. 

Adoption and usage of machine data analytics growing at every business level

Amongst the study’s key findings, a staggering 96 per cent of respondents said machine data is either extremely important or somewhat important, with over 54 per cent stating their company already uses machine data tools for enhanced business insight. 

While IT operations (70 per cent) still tops the list in terms of ownership and current usage, the study revealed a surprising number of other roles that use machine data analytics on a regular basis as well. This includes developers (56 per cent), product managers (46 per cent) and customer support (44 per cent), right through to the CEOs (34 per cent), and cloud architects (31 per cent), demonstrating a keen understanding of its value at almost every level of business.

Interestingly, the more software-centric a company is, the more likely it is to have more than 100 employees using machine analytics at least once per week, indicating that these companies recognise its value more than others at present. Software-centric companies can be defined as those businesses that use applications to execute business logic and rules directly rather than always requiring human interaction to make decisions. Software-centric companies are also more likely to integrate their business intelligence and machine data analytics tools, to further enhance their intelligence gathering capabilities.

Product development and customer experience are key areas of focus

When asked where the most potential to expand the use of machine data analytics was within their business, over half of respondents (53 per cent) identified more efficient product development (i.e faster time to market) as their top priority. This was closely followed by smarter product development based on insight into customer usage (50 per cent), improved application uptime through better issues troubleshooting (47 per cent) and improved customer experience by fixing issues before customers are impacted (44 per cent). 

There is therefore a growing emphasis being placed on using machine data to achieve a competitive advantage, make smarter decisions around product direction and drive the best possible customer experiences for their end-users.  

However, the data also revealed several frustrations amongst respondents and barriers to the expansion of machine data analytics. Perhaps unsurprisingly, the number one complaint was ease of use - 44 percent of respondents said their current tools lacked the self-service capabilities that make it easy for anyone to create reports and understand insights. Many businesses still choose to centralise the use of analytics tools within  a small number of ‘data experts’, tasked with servicing wider teams who lack the expertise and training to use the tools themselves. 

This can result in a bottleneck situation, limiting the amount of value that can be gained from the time-sensitive data while it’s still valuable. The lack of access to data in real-time was also cited as a barrier to success for many respondents, which can be critical when supporting issues relating to customer experience and continuous intelligence.  

Making tools easier to use eliminates these kinds of issues and allows for the democratization of data, giving a far greater number of employees access to real-time data and insights to use within their various roles. This availability helps employees act on insights and make smarter, data-driven decisions.

New technologies hindering broader usage, but not as much as thought

Interestingly, the study revealed emerging technologies such as cloud, containers and microservices are holding back the broader usage of machine data analytics, but not to the extent that was anticipated. In total, 44 per cent of respondents said the adoption of technologies such as those mentioned above does make it harder to identify and extract the data needed for fast decision making. However, this is a lower percentage than was expected. 

This indicates the vendors behind these new technologies already recognise the issue and are beginning to develop tools that enable better insight into these complex environments. The emergence of dedicated machine data analytics platforms is further simplifying the process, delivering secure and scalable solutions that provide a real-time view into the modern application stack.

The age of digital services is putting pressure on companies to be faster and more agile than ever before, resulting in a growing reliance on effective data analytics. The scale, speed, certainty and diversity shift is defining a new analytics economy, in which companies will win or lose based on how well they consume, and act upon, insights from analytics to drive innovation, improve customer experiences and inform decision making.

At present, the larger, software-centric companies lead the way, using machine data analytics in innovative ways to drive business efficiency. But as adoption continues to grow and technology vendors further reduce the barriers to entry with easy-to-use and dedicated solutions, expect to see machine data analytics play an increasingly pivotal role in business strategy in the years to come.

Colin Fernandes, Director of Product Marketing, EMEA at Sumo Logic

Image Credit: ESB Professional / Shutterstock

Colin Fernandes is director of product marketing EMEA at Sumo Logic. He leads the company’s education and marketing campaigns in Europe, helping companies understand the challenges and opportunities around modern application design and implementation at scale. Prior to Sumo Logic, Colin worked at VMware where he was responsible for the company’s operations around the telecoms and cloud management sectors.