Ram Vaidyanathan sets out a step-by-step approach to extracting true value from big data.
In the year 2002, Billy Beane and Paul DePodesta famously used the principles of sabermetrics to assemble a competitive Oakland Athletics team. They decided not to opt for the big stars. Instead, they recruited players who cost much less, but who the data showed could be a winning combination. The A’s soon went on a 20-game winning streak in Major League Baseball.
If data analytics can leave such a big mark in sport, it can certainly do so in business where subjectivity plays a smaller role. However, in today’s digitally enabled businesses, data is much more complex (and bigger) than what DePodesta and Beane had to contend with. The secret to maximising business value with big data lies in how companies use IT tools to extract, process, analyse and visualise it. Let’s look a little more into this process.
Extracting the data: The first step towards maximising business value with big data is to actually obtain the data. For this, businesses need to extract both structured and unstructured data, and hopefully lots of it, from a range of sources. Sales reports, customer on-boarding dates, SLA breach rates, and web logs are examples of structured data. Social media posts, email messages, PDF files, and multimedia are examples of unstructured data. Unstructured and structured data each have their own strengths and weaknesses, so it is important to include both types in order to see the whole picture.
One example of pulling data from a range of sources is Duetto, a company that helps hotels personalise their prices by extracting and analysing historical data such as how much a guest typically spends at the bar or casino. Hotels can then incentivise guests with better room prices knowing that those guests will spend more on other services.
Setting relevant formulas and targets: Once the data is extracted, it should be contextualised. Each facet of business should decide what metrics they will track and benchmark themselves on. For example, a sales leader will be most interested in tracking sales volume over time, sales volume by region, and purchase values of top customers. A marketing leader, on the other hand, may be most interested in metrics such as ROI, advertising reach, and profit margins over time. On top of those, an IT leader may be most concerned with SLA compliance rates, user satisfaction scores, and network reliability.
Analytics tools should be flexible enough to allow users to set their own parameters and criteria for measurement. Beyond that, users should be able to create custom formulas to meet their reporting requirements quickly and easily with built-in functions.
Analysing data for patterns: After data has been extracted and contextualised, the analysis part comes in. First, analytics tools should alert users of any outliers and offer to exclude these outliers from the analysis. And, these tools should make sure to consider entire datasets, rather than sample subsets. Users should also be able to set up advanced techniques such as correlation analysis, regression analysis, conjoint analysis, and factor analysis, if required.
For example, marketing and sales teams may want to analyse the relationship between the total sales in a given time period and the advertising budget in the previous time period. A regression analysis could show how the two variables are related and help both teams correctly allocate their budgets.
An IT manager may use an analytics tool in a very different way. They may be more interested in the percentage of devices in error by business function. For instance, they may be able to see on the tool’s dashboard that a certain sales office has the highest percentage of faulty devices. This could signify an underlying problem that needs to be discovered and solved.
The power of big data analytics is very apparent in the case of Starbucks. It uses big data to determine the potential success of every new location prior to expanding its operations. With data based on location, traffic, demographic and customer surveys, Starbucks can estimate the general success rate for each new store.
Driving business insight: In the end, even contextually analysed data can’t do any good if it’s not being properly utilised. And when it comes to getting the most out of your data, it’s all about how you see it. Whatever big data analytics tool companies deploy should use data visualisation technology to help decision makers identify patterns. For example, a CIO might benefit from a dashboard that shows the number of unsatisfied customers over a period of time and lets them review the areas of the highest customer dissatisfaction. Visually representing these issues, with options to drill deeper into each graph, will help the CIO fully understand the situation, even at a glance.
To completely enable visualisation in an analytics tool, users should be able to create graphs and dashboards in real time by dragging and dropping customised and out-of-the-box data fields. They should be able to combine multiple reports into a single, live dashboard, eliminating the need for multiple tabs and simplifying analysis. Analytics tools should also bring different data sets together on the same screen and show any existing relationships.
Users should also be able to create a wide range of charts such as line, bar, stacked bar, pie, and scatter, and it should be easy to customise titles, colours, legends and tool tips. Effective visualisation tools can help you clearly identify problem areas and save a considerable amount of time in the decision-making process.
Even a small start in big data can give big returns
Geoffrey Moore, author and management consultant, says, “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
There is no dearth of data in today’s digital world. But, businesses will only be able to derive maximum value if they make full use of this data. Even organisations that don’t see the need for big data analytics just yet should start small and invest in basic analytics tools. Even a small start in data analytics can produce big returns.
In the future, the complexity of data will increase as technologies such as smart devices, augmented reality and the internet of things become common. While the challenge of monitoring so much data will remain, the new challenge will be to make big data analytics available to every employee. This will empower them to make smart decisions within their realm and create even more business value.
Ram Vaidyanathan is regional manager, ManageEngine.