Big Data, Big Business: Is IT the key to better efficiency in Qatar?
The Information Age is presenting organisations with massive amounts of data, but questions remain about the real usefulness of it all. How can this data be exploited and utilised to improve efficiency and profits? Charles Vincent, general manager of A101 Digital Solutions in Qatar, explains to The Edge how data analysis can improve business efficiencies across all sectors including IT, retail and in human resources (HR) management especially. By Mark van Dijk.
Companies across all sectors in Qatar have yet to fully realise the potential contribution of big data analysis when it comes to improving efficiencies in operational functions such as human resources and in optimising retail offerings, to name but two. (Image FotoArabia)
It happens all around us, all the time, and often without our knowledge. Whenever we travel, whenever we purchase something, whenever we walk through the security gate at the office: thousands of machines track our every move. This is not the premise of a paranoid science fiction film; it is the very real modern world of Big Data – and it is changing the way humans do business.
Big data is not a new term and of course is used to describe data sets that are so large or complex that traditional data processing applications are inadequate. This information is usually generated from sensors and machine-to-machine (M2M) technologies within a facility. According to analyst firm Berg Insights, the number of M2M technologies in use in the global oil and gas sector could rise from 423, 000 in late 2013 to 1.12 million by 2018.
“The greatest value of this data gathering lies in the compounding impact,” Ghassan Barghouth, vice president, Middle East at Schneider Electric, wrote in a recent statement. “Building an organised portal of useful data today places a company at the forefront of innovation in five or 10 years. These systems have evolved and matured to operate alongside today’s IT systems and standards.”
Yet despite the obvious benefits of this high level of data analysis, companies in the Gulf and across the world are proving to be slow adopters. “IT leaders are looking twice, even three times, before riding these new technology waves into their mission-critical core applications,” analyst Joe McKendrick remarked during his opening keynote at the Collaborate 2016 conference in Las Vegas, United States, in April. McKendrick based his opinion on a survey of nearly 700 users of computer software firm Oracle, which hosted the conference.
Charles Vincent, general manager at the Doha-based A101, has noticed this trend too – and understands the reasons for the scepticism. “A lot of time people consider technology as expensive and as changing so quickly that it’s like a never-ending hole where you just pour money in just to look fancy,” he tells The Edge. “On the other hand, when you come from an IT background, you have the complete opposite approach where you believe technology is helping a lot. One of the reasons is because it tries to translate any actions or activities or processes within the company into data. When this data is measured and well analysed, we can really put our finger on areas where there is a lack of efficiency.”
A101 has developed a line of products focusing on big data analysis that are aimed at improving business efficiency, including HR time tracking, retail footfall analysis, incident reporting and event activities. Vincent believes that Qatar is perfectly poised to embrace data analytics – precisely because of, and not in spite of, the state’s economic pinch. “In previous years, Qatar was in a high pace of demographic and economic growth,” he explains. “There was no time to waste focusing on how things were operated, and companies had to adapt very quickly. Many burned through some necessary implementations of good practices, which is normal in any fast-growing economy.”
These companies enjoyed many years of very good growth, sustained by the high oil and gas prices, says Vincent. “Now that the deal has changed,” he adds, “I think everyone is expecting to sell a bit less while keeping profitability afloat. These businesses will tend to look at themselves, how they operate, and look to optimise and reduce costs to hopefully keep the same profit margin as before.”
Vincent’s point of view is backed up by recent global findings. A new survey conducted by the Business Application Research Centre (BARC) revealed the value of big data analytics in helping organisations to become more resilient in the face of increasing cyber attacks.
“A recent survey found that 53 percent of organisations that are using big data security analytics report a ‘high’ business benefit,” says BARC founder Carsten Bange. “The survey also found that 41 percent reported a ‘moderate’ benefit and only six percent said benefit was ‘low’, so there is fairly strong evidence of the business benefits of big data security analytics.”
Bange adds that cyber security resilience can be enhanced by user behaviour analytics: in other words, by tracking user behaviour across all IT systems to identify significant deviations from normal behaviour and to warn of potential malicious activity. “There is nothing new in being able to identify patterns of behaviour,” Bange continues. “Most of the analysis techniques are 30 to 40 years old. But now we are able to apply them to extremely large data sets across multiple information technology systems.”
Another area of business big data analytics can be implemented with a direct positive effect on turnover is in retail. While this is not new in Qatar, concedes A101’s Vincent, “for some reason it has not widely been implemented yet here, but now that there is a lot more retail offer than has ever been in Qatar, people might be focusing on this a bit more, as they should. It consists in measuring footfall so you know how many people at the end of the day came inside the mall or store, but more importantly we want to know where those people have been and how they have behaved.”
Of course, behaviour analytics need not be limited to security or retail. At A101, Vincent and his team have identified other key areas in which Qatar’s data analysis transformation could happen: your own staff. “Those sectors are mainly found in human resources (HR),” he says, “mainly looking at the overtime of employees, where there is no proper ways of measuring this.”
Vincent explains further: “When we measure people in their work space – outdoors or outside – the point is really to measure their ins and outs, and to be able to quantify the time this whole workforce represents. Everyone has problems with overtime in Qatar. Overtime costs a lot of money for a company, so having a system that doesn’t cost much at all, which has a fairly quick return, is very important. Taking the example of companies that have 250 to 3000 or 5000 employees, if you are talking about 30 percent of overtime, then 20 percent might be justified, but if 10 percent is not, then that represents a lot of your payroll.”
“An interesting example,” Vincent continues, “is when a biometric – a machine reading fingerprints at the entrance – is installed. Now people start coming back on time, because they feel they are watched. They think, ‘I’d better be here for the machine!’ Now, in a lot of cases we came across, those machines were not even plugged in and nobody was taking the reports from it, but still the psychological effect was working.”
Continuing the example, Vincent expands this to a larger company, perhaps of 100 employees or more. “If those machines are not plugged in,” he says, “and rumours start going around: IT finds out, laughs about it, discredits the management team, and nobody cares anymore. That is generally when they put a reporting system in place, which is basically an Excel file which they download from the machine every week or two.”
Even then, though, the information is gathered, processed and saved by the IT team… and, as Vincent says, “that means humans are taking care of it”. Human error – whether intentional or not – remains an error. And therein lies the clear benefit of computerised data analytics.
“Finally you introduce a system where the report leads directly to a cloud server, and gives the proper report at the end of the month,” Vincent adds. “It is completely automated. It does not require anyone. It gives a report at the end of the day, week and month of people, which allows us to calculate and quantify the amount of overtime along with in depth analytics, which can be compared on an employee or department with the company’s key performance indicators (KPIs). It also gives you all the data you require to do your accounting around Qatar’s newly-implemented wage protection system.”
Quality vs. quantity
As Vincent’s case study illustrates, any quantity of data is useless without an adequate quality of data. As he puts it: “Gathering the data is one thing; filtering this data, analysing this data…. That’s another thing completely.” The obvious take out is that in HR or in oil and gas production, the real value of data gathering lies in the analysis of that data.
“It is important to say that a lot of people do things traditionally on paper,” Vincent warns. “I’ll give you a simple example. I am living in an apartment. I want to report an incident. There’s a leak, so a guy comes and he makes me sign a piece of paper. He comes back later to sort out the leak. Everything is fine, except that he has taken four days to do it. At his next weekly meeting, he is going to say everything was done and cleared out fairly quickly. It was quick for him, but from a higher management perspective, if it leaks in 50 apartments in the same residence, then there is a major problem. It is not localised in one apartment, but it is a bigger issue that the landlords need to revise.”
Another problem with paper records is that the computational factor is removed entirely from the equation. Files upon files of paper records cannot be analysed; the values cannot be compared, charts cannot be created and comparisons cannot be drawn. “Finding patterns, when it is done on paper, is just impossible,” says Vincent. “Let’s face facts: the manager is not going to look and run statistics through 50 pieces of paper.”
However, if the data is entered in an electronic system, Vincent’s hypothetical building management team will be able to measure the efficiency of the plumbing team, record the time it took to fix the problem, and track the types of problems that are recurrent. “A recurring problem costs money, right?” he says. “So if you fix it once and for all you will save in the longer run.”
Many companies are still hesitant to embrace Big Data, or to explore the possibilities of data analytics. As McKendrick’s snap survey (and Vincent’s own experience) suggests, there are concerns around the costs and the mechanics of implementation. But we are fast reaching a tipping point, where the challenge is less about whether a company can afford data analytics and rather whether any modern concern can afford to go without any data analytics.
“Everyone should know as much as they can, so that they are more aware and more dynamic in terms of decision-making, and more prompt to take quicker decisions,” Vincent concludes.