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September 3, 2015

Unleashing the power of data analytics to drive improved performance

Charles Adriaenssens, Analytics lead, EMEA at Splunk, discusses the best practises in analysing and interpreting company data.

By Cbr Rolling Blog

We are living in a world that is rapidly evolving. We are more mobile and connected than ever, and we are capturing more data than ever about the performance of our business from an ever-increasing number of sources. In the modern business environment, the ability of companies to derive (fast) insights from heterogeneous and complex data sources is starting to become a key determinant of business success.

The ability to realise business value from data requires access to the appropriate software, as well as individuals who understand what the data means to interpret the findings; and the importance of having the right software and the right people grows when the complexity of data and / or the number of data sources used increases.

One of the biggest challenges for businesses right now is that the data they need to interrogate frequently includes both unstructured and structured data. In fact, the fastest growing data source is unstructured, machine-generated data, which can be defined as any data that is generated directly from machines. Many elements of the business infrastructure now generate massive streams of data in a variety of formats that are difficult to process and analyze by traditional methods in a timely manner.

To make this situation even more challenging, the number of sources of data is increasing as well, driven by the Internet of Things. Many consumers now own multiple data-generating products, including computers, tablets, mobile phones, digital TV, cars and watches. In the same way, the standard operating environment of a company now typically includes a complex network, a range of applications and multiple access devices.

Every single action by each device interacting with a company’s operating environment leaves an event record, which comprises individual pieces of information that record key details of that activity. For example, every web page viewed leaves a log of user activity, including a source, a page reference, a date/time stamp and a status code.

Machine-generated data has been given a turbo boost by the growth in ownership of the data-generating products, and this has led Gartner to predict that "by 2017 over 50% of (business) analytics implementations will make use of event data streams generated from instrumented machines, applications and/or individuals".

This growth in complex machine-generated data is why any company looking to maximise the value from its data sources needs to select the right combination of tools and experts to analyse it. Often the richest insights will be generated by linking multiple data sources, so having the capability to do this will dictate software and personnel needs.

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Companies are becoming increasingly willing to invest in powerful software, as well as teams of individuals to analyse and interpret their data. The rewards for success are significant. A decline in the proportion of customers who experience operational issues during the customer journey, or an increase in the proportion of customers who respond to marketing activity as a result of appropriate messaging, can have a significant impact on business performance.

While the investment required to achieve performance improvements can be significant – involving the purchase of any software required, as well as investment in multi-disciplined teams to set up and extract insights from any data generated – the rewards typically outweigh any cost.

With the focus for many companies being on improving the brand experience for customers across all touch points and channels of interaction, those companies which are able to capture, analyse and unlock the value of their data will have a distinct competitive advantage as data volumes increase over the next few years.

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