Money, according to Liza Minnelli in Cabaret, makes the world go round. But as paper notes and metal coins are replaced by zeroes and ones on a computer screen, and with almost every other aspect of life now as reliant on the digital plane as the physical, it’s fair to say that these days data makes the world go round.
In the business world in particular, the last couple of decades have seen data explode in importance. The commercial sector has realised that the answer to almost any business question can be found in what’s known as ‘big data’, provided there is an efficient way to collect and make sense of the masses of information. Many businesses have the data, whether data mining from their business systems (accounting, CRM and analytics to name a few), marketing efforts, or market research. But they are unable to utilise it, to empower it, for the simple fact that with so much data they don’t know where to start.
“One trend is clear,” notes Yun Zhi Lin, Head of Engineering (APAC) at innovation consultancy Contino: “Organisations are increasingly becoming more data-centric, and demand more prescriptive insight to make strategic decisions based on data, as opposed to relying on their gut feelings.” But actually getting this insight can prove quite the hurdle.
Thankfully, help is at hand. The likes of business intelligence (BI) and robotic process automation (RPA) solutions look set to bring big data to the masses, allowing organisations of every size to enjoy the incredible insights that such data can offer up. To find out more about these solutions we spoke to a number of experts, like Lin, who are waist-deep in this burgeoning field.
Business intelligence vs business analytics
To understand how an organisation might be able to empower their data (and subsequently themselves), we first need to understand a couple of the important – and often confusing – terms used in the field. And as umbrella terms that are applicable to almost all organisations, business intelligence (BI) and business analytics (BA) are two of the first that most new entrants into the world of big data will stumble upon.
“Business intelligence and business analytics are often used interchangeably,” says Lin. And it’s true that, to an outside observer, there would seem to be very little difference between the two. But understanding their subtle differences is key to making the most of your organisation’s data.
The data team at OpenAgent defines them thus: “Business intelligence is around core business performance and is used as a diagnostic tool to better understand and provide insight into the operations of the business. Business analytics is about the why and the how, and tends to take a more exploratory view in order to identify opportunities and predict outcomes.”
Dr. Cher Han Lau, Chief Data Scientist at iStream360, further simplifies the distinction between the two. “In broad terms, business intelligence systems are used to optimise and streamline current operations. They focus on what is happening to our business. Business analytics systems utilise statistical analysis, data and quantitative analysis for investigation and predictions. They focus on why it is happening, and what will happen in the future.”
In the most basic of terms, BI analyses data for operational purposes, while BA analyses data for strategic purposes.
How robotics process automation enables better data management
Humans are great at a lot of things… just not mundane, repetitive, computational tasks. In order to cope with the often terabytes of data used in BI and BA systems, we need to automate processes and get smart tech to do the stuff we’re not so good at.
Well managed and maintained data is key to reaping the rewards offered by big data. As Lin says, the “machine generated intelligence, descriptive insight, predictive forecast and prescriptive actions [that business intelligence tools generate] will only be as good as the data that is fed in.” But managing data has always been an incredibly labour-intensive affair, with constant checks and updates required to maintain its integrity.
Robotics process automation (RPA) seeks to solve the issue of data management (along with a whole lot else). RPA refers to software that can be easily programmed to do basic, repetitive tasks across applications, just as if a human were manually tapping the keys and clicking the mouse at a workstation. RPAs can be taught workflows, and can therefore be used to fill in forms, update spreadsheets, file away documents in the correct location, and do any other basic, repetitive task.
According to Jeff Olson, Head of Applied AI & Analytics at Cognizant ANZ, this type of automation brings two major perks: “Speed and quality. The ability to automate routine tasks, improving both speed and error quality, is a primary benefit of automation, as it frees people to consider further improvements and new areas for customer engagement.”
Automation: business intelligence’s secret sauce
Managing data is one thing; actually gaining insights from it is entirely another. The recent explosion in data has been driven by the cloud – with storage space no longer an issue, you needn’t pick and choose the data that you keep, you can just keep all of it. But this has meant that the growth in the data available to business leaders has totally outpaced their ability to make sense of it. Excel wasn’t built to deal in terabytes.
Here, too, automation is key; this time in the form of business analysis automation (BAA).
BAA uses a set of tools to draw data from disparate sources across your business, then develops insights that are delivered to you every day. Perhaps it picks up an unexpected change in customer behaviour, customer demographics, or a lull in conversion rates. Instead of wondering what might be hiding in the data, you’re instead sent a breakdown of every opportunity or issue at the start of every workday.
Like RPA for data management, BAA for data insights isn’t designed to replace people, but rather remove the repetitive, laborious work of sifting through endless data. “The Industrial Revolution of the 18th century automated processes to achieve higher productivity,” Han points out. “The primary outcome of automation is to increase the efficiency and effectiveness of performing basic tasks.” In BAA the golden insights are automatically found, and your time is spent acting on them rather than searching for them.
The value of business intelligence in ERP systems
Responsible for the integrated management of core business processes, enterprise resource planning (ERP) systems, sometimes called business management systems, hold all the information an organisation needs to function. In the past, ‘business intelligence’ within an ERP systems was an all too manual affair – drawing data from multiple sources, putting in a spreadsheet, manipulating it, then producing an entirely static report.
But as big data tech has developed, BI modules have begun to be integrated into ERP systems. This has allowed for instantaneous and dynamic reporting – users are delivered the insights that they want, when they want them, built with real-time data. Easy to use dashboards allow the data to be sliced and diced with ease, meaning decisions can be made quickly based on facts, not gut feel. Much like BAA for data insights, BI in ERP can instantly serve up insights that a human being might take a century to find by manually searching through a mountain of data.
Along with BI, robotic process automation also has a huge part to play in ERP systems. According to Olson of Cognizant, the potential benefits of RPA in ERP are seemingly endless. “Improved processing and automation is applicable to almost every enterprise task. As an example we helped a customer optimise vendor payment terms processes and reduce days to pay by 3%-5%, achieving tens of millions in savings, while importantly not harming the business relationship. AI assisted terms evaluation along with compliance monitoring reducing leakage were some key improvements that enabled this outcome.”
Empowering your team to empower your data
While the potential of big data is undeniable, no data effort will be successful without buy-in from those working on the frontline. “Organisational change is often viewed as an isolated and static event – but it is dynamic, ongoing, and influenced by the people participating in it,” advises Nick Himonas, Senior Consultant of People Advisory Services at EY.
Lin concurs: “A successful data and applied intelligence strategy requires a combination of data-ready culture, enterprise-wide buy-in, skilled team members, and the right initiatives.”
Data Lead Celina Quizon from OpenAgent says access to and understanding of big data tools are vital if they are to be properly utilised. “Being an internet start-up company, it’s essential for OpenAgent to have a real-time view of its performance. Everyone in the business has access to our BI tools. We’ve created tailored BI and data visualizations for each business area so that they have a view of how they are tracking.”
Han has high hopes for iStream360’s use of big data and automation tech into the future. “Eventually, we want to replace 70%-80% of our business operations with artificial intelligence. That doesn’t mean that we will reduce 80% of our employees; just that we want to minimise the room for error and make better use of their expertise and creativity.”
Access to data is no longer a problem – most organisations now have more than they can poke a stick at. The factor that will separate the best from the rest will be the willingness to adopt new tech that can help to make sense of all those zeroes and ones – to turn the mountain of otherwise meaningless information into insights that can make a real difference.
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