There are pretty clear signals from corporate Australia and New Zealand that they are onboard with data and the importance of enabling an internal data culture.

But they face the same headwinds as executives in other parts of the world to make good. PwC’s recent CEO survey is indicative of the challenges they face.

“Despite billions of dollars of investment and priority positioning on the C-suite agenda, the gap between the information CEOs need and what they get has not closed in the past ten years”, PwC found. “Organisations are struggling to translate a deluge of data into better decision making.”

I’m often asked by executives what they should be doing to create a better data culture in their organisations or to become more data-driven.

Data doesn’t drive anything. It’s people that drive the success or failure of data projects.

The phrase data-driven culture is being used everywhere, but data doesn’t drive anything. When you get to the bottom of it, it’s people that drive the success or failure of data projects.

Many companies just let culture happen; very few intentionally work on it, the Harvard Business Review says. Yet those that actively work to cultivate an organisational culture – the ones that find the enablers and weed out the blockers – are more likely to have engaged workforces.

Cultivating an internal data culture is no different. If you haven’t already done so, it’s time to identify and address blockers that are impeding your progress.

There are five common blockers I see in local organisations trying to develop a data culture.

1. Too many excuses

People are good at finding reasons not to share or open up data reserves.

Internal departments like to hold onto their own data. They might feel there is limited benefit to them in opening it up to other internal users. But the magic really happens when you start collaborating and joining things across different datasets.

Executives need to find a way to break that down and to find an equation where everybody shares and wins.

In addition, people often hide behind security and privacy as an excuse not to share data. These are, of course, critical considerations and conversations, but they are often misunderstood.

The key is good governance. Done correctly, governance should enable people to get to the things they need to get on with their work. However, most people think of governance as locking things up and preventing them from accessing things.

Executives must redefine governance. It should be about ‘How can I give as much of this to people as possible?’ If there isn’t a legal impediment, the default should be to let people see and access data.

2. It’s not my domain

Data is still treated as a carve-out discipline in many organisations. It’s a technical domain for a limited set of data scientists and business analysts. This is a common blocker.

The reality is that everyone is a data person in some way. And if you become more interested in it, data is going to help you. It will make your work life much easier. It could even mean going home a bit earlier every day because better insights and decision-making support is at hand.

Executives need to find a way to show this to people ­– and the best way to do that is to put the power of data into people’s hands.

3. Not building community

Internal champions are critical to data success.

Taking data culture organisation-wide requires top-down leadership, but it also thrives on internal champions -– people who see the benefits and potential and can be subject matter experts in their own divisions or teams.

Community is massive. The most successful data deployments are the ones where there is a strong community. You need champions who will stand up on a soapbox and talk about their success, and create user groups and a community of practice events. Internally, things then spread.

4. Lack of buy-in from the top

As an executive, you’re onboard with data, but are your peers? And what about the people managers one or two layers under?

We all know people will defer to the HiPPO (Highest Paid Person’s Opinion) in the room. Data initiatives won’t go anywhere without that buy-in.

Executives should also be involved in these meetings. Having representatives attend instead can make it difficult for the people involved at lower levels of the organisation to gauge the receptiveness of the C-level to data-driven proposals.

5. People aren’t inquisitive enough

One thing I’m often asked by executives is how to create a culture of curiosity.

‘Where do I find these people?’ ‘What sort of recruitment policies do I need to have to have my team do this?’ ‘How do I make existing employees ask more questions?’ ‘How do I make them be curious about data and discover things that they didn’t know were even there?’ are just some of the questions I hear.

A recent survey by Harvard Business Review found about 70 percent of workers face barriers to asking more questions at work. Executives were often the problem, prioritising efficiency over exploration, perhaps even subconsciously relegating exploration for fear it will slow business-as-usual progress.

The obvious solution is to make exploration easier. If data is hard to query and tools aren’t available to help people visualise data, test hypotheses and keep asking questions, they won’t. Giving people a way to visualise data and to question it whenever they like could bring results.

ABOUT MAC BRYLA//
Mac Bryla is APAC Technology Evangelist at Tableau Software.

There is an emerging theme that smart technologies will augment what people do and make them more effective.

This theme is most apparent in the subtle retooling of the AI acronym from ‘artificial intelligence’ to ‘augmented intelligence’ over the past year.

But treating smart technologies as an augmentation of the human form is also a smart mindset in which to frame its future development.

“New AI and ML capabilities will enhance human creativity and ingenuity, not replace it.”

It’s an extension of – rather than replacement for – our brain power. It should be developed only within the express purpose of making our lives easier.

What’s this all about?
How far we take augmentation has been the subject of fierce debate and occasional experimentation for at least the past two decades.

Augmentation owes a lot to philosophers Andy Clark and David Chalmers and their seminal 1998 paper, ‘The Extended Mind’. In it, they explored the boundary of ‘self’ and the extent to which tools and technology could be considered to extend that boundary.

The theory got a kick in 2014 when a US court concluded that “modern cell phones . . . are now such a pervasive and insistent part of daily life that the proverbial visitor from Mars might conclude they were an important feature of human anatomy.”

That has allowed an idea to permeate that smartphones are such an integral part of life that they have become part of our bodies and minds. They usefully extend our human capabilities and have taken on the status of an accepted augmentation to our being.

Some people have taken this a step further by actually implanting technology within the body; a NSW man inserted a transit card chip in his finger. Others have inserted small chips in their finger that let them open doors or operate light switches.

Indeed, another argument is that humans effectively need to become cyborgs to compete with the robots of the future, augmenting their capabilities through a ‘merger of biological intelligence and machine intelligence’.

While that is clearly a long-term view, the growth of machine intelligence means we have an opportunity to observe how willing humans are to accept it.

No room for replacement
Smart technologies are also permeating the world of analytics, and the creators of smart analytics and its buyers must now convince employees that the technology is actually an augmentation of their existing skills and capabilities.

While some in the industry believe AI technology will replace the data analyst (because it will be superior to people when it comes to drawing insights from data), at Tableau we believe that new AI and machine learning capabilities will enhance human creativity and ingenuity, not replace it.

There are a few reasons why we believe this to be the case.

First, in most projects today, humans are still required as a point of escalation. Arguably this plays to strength of both machine and human, handing off the most repetitive of tasks to the machine while leaving the human to step in for only the most complex of cases.

For example, analyst firm Gartner predicts that virtual agents will “take over simple requests and tasks from a call centre, help desk and other service human agents, while handing over the more complex questions to their human counterparts.”

That will evolve over time. By 2022, Gartner has said one in five workers engaged in mostly non-routine tasks will rely on AI to do a job.

“Companies are just beginning to seize the opportunity to improve non-routine work through AI by applying it to general-purpose tools,” the analyst firm said.

Fellow analysts Constellation Research see AI-powered advances “as early signs of a new era of computer-assisted analytics.”

“Smart capabilities complement human interpretive skills with computer processing power that can be harnessed to automate repetitive tasks and tackle complex calculations,” Constellation said in a recent report.

“What’s more, machine learning and natural language understanding augment the analytical skills of employees and customers.”

The second point to make here is that machines still require human overseers to validate that what they decide is correct. The machine’s output must be constantly reviewed to ensure its accuracy remains high.

Humans with the domain knowledge in the business remain the best people to perform this task.

Gartner believes this kind of decision support/augmentation “will have surpassed all other types of AI initiatives to account for 44 percent of global AI-derived business value” by 2022.

It is clear that smart analytics technology is ready to act as extension of our bodies and a way to address our physical limitations.

The challenge now is for people to upgrade themselves and their capabilities with augmented intelligence – or to at least let their employers get on with the job of doing it. Either way, the workforce stands to benefit.

ABOUT MAC BRYLA//

Mac Bryla, APAC Technology Evangelist at Tableau Software, based in Sydney.

Has your enterprise appointed a chief data analytics officer (CDAO)? Those which don’t may soon find themselves out of step with the majority. Indeed, Gartner has predicted that by 2019, 90 percent of large organisations globally will have someone in the seat.

In common with their counterparts around the world, there is also an emergence of CDAO roles in Australian companies. One only needs to look at the banking and finance sector here. Many institutions now have a head of data and analytics, including NAB, HSBC and ANZ Bank. But it doesn’t stop at the banks with companies in many other sectors also taking steps to capitalise on the value of analytics, including Bupa, Telstra, NBN Australia and Origin Energy.

“It means IT staff no longer ‘own’ the data analysis function but there’s potentially great gain for the business.”

Key to gauging the effectiveness of the person who’s allocated to oversee this activity, whether it be on a part-time or full-time basis, is determining exactly what it is they’re responsible for – and who they should report to.

This hasn’t been simple to define.

Traditionally business intelligence has found a natural home in the IT department, under the mantle of the CIO, but as its presence becomes more pervasive across the enterprise, the boundaries have begun to blur.

This is exactly as it should be.

The CDAO’s role should be aligned exclusively to neither IT nor business; it must straddle both in order to be effective. Part of the remit should be to break down barriers that prevent valuable insights being delivered to business units in a timely fashion.

Wherever they sit on the company’s organisational chart, what the CDAO should be accountable for is becoming increasingly clear – harnessing the value of the organisation’s data and ensuring the insights derived from it are accessible to the right people, at the right time and in the right context.

This sounds straightforward enough but, in reality, it can be anything but. In some cases, changing the rules to allow additional employees or business units to access data can be met with uncertainty, doubt and resistance. This is particularly the case when there’s an ‘us and them’ culture, with clear demarcation between IT and business.

Rather than letting these traditional divisions drive decisions around who can access data and perform analytical tasks or where responsibility for governance lies, it’s the chief data officer’s job to think broadly, disrupt the status quo and champion the reasons for doing so.

Their long-term goal should be the creation of a digitally literate workforce and corporate culture, which sees data analysis being performed by employees from all divisions within the business.

Yes, it means IT staff no longer ‘own’ the data analysis function but there’s potentially great gain for the business in their giving some of it away.

With the aid of modern, intuitive analytics tools, many business users are more than capable of sharing the responsibility for data analysis. In lieu of deep technical expertise, they can bring other valuable skills and specialist business knowledge to the task.

The diversity of thought that results from technical and business minds working together can lead to greater innovation – a win for any business in an age where businesses need to be able to adapt rapidly, in order to survive and thrive.

Given their remit is to blur the lines between business and IT, evaluating the CDAO’s performance using traditional IT metrics, such as time taken to load a data warehouse or distribute reports, makes little sense. Rather, they should be judged by the contribution data analytics has made to business outcomes. Yes, it’s harder to quantify but in today’s data driven business world, it’s the only thing that matters.

ABOUT MAC BRYLA//

Mac Bryla, APAC Technology Evangelist at Tableau Software, based in Sydney.