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How to win with data science

Published first on November 8, 2018 on

Big Data World reflections

Cyviz recently participated at the Big Data World event in Singapore with our strategic analytics partner – Tibco. Together we showcased how combining analytics software and a designated physical space can unlock the maximum value from an organization’s data science efforts.

We live in a changing world where data is the oil that is fuelling a digital economy. At Cyviz, we see a strong commonality across all the verticals we work with, be it oil and gas, FMCG, security or the ever increasing corporate sector. The unifying factor between all of our customers is their dependence on data.

Organizations are leveraging increasingly larger volumes of data, employing Data Scientists who are applying machine learning, AI, and using analytics software to drive insight and make recommendations from the data which they then use to make decision and take the appropriate action.

The formula is not new, however the process to get from Data –> Insight –> Action is becoming ever more refined. With the advent of highly sophisticated technologies, capable of processing huge data sets in near real-time and making this data available to the right person at the right time, the opportunity is even greater.

At Big Data World I witnessed, and realised that data science is capturing the imagination of a generation and moving from a geeky (geeky is also cool by the way) and highly technical discipline to an uber cool business enabler at the heart of the digital transformation shift.

No longer a world owned solely by the IT department, data science has the potential to rapidly transform a company’s fortunes, deliver huge efficiencies at every stage of the value chain and create new business models previously unthinkable. With initiatives such as #shelovesdata (, data science is inspiring and encouraging a new breed of tech savvy practitioners and their value to an organization increases with every packet of data processed.

My key takeaway from the show is that the industry is ready for “Action” as it is the “Action” stage of our model that delivers the most value. This is where we want to be as this is where innovation and transformative ideas become a reality.

I therefore have two questions for the community:

Are we spending enough time where it matters – taking action?

Are we providing our teams with the right tools to be successful?

Data governance and enablement

As pointed out by IDC:

“Maximizing success in digital transformation (DX) requires organizations to be enabled by data, not restricted by data governance. But as the need to leverage data escalates, so too does the need to protect data within complex ecosystems, business, IT, and data environments.” 

High profile cases involving internet giants like Facebook and Google as along with regulations such as the General Data Protection Regulation (GDPR) is making data governance a topic of social discussion and is no longer an “optional” practice for organizations.

Just like the energy sector, searching for and extracting oil, then processing and refining their raw material whilst ensuring it is protected at every stage of the chain, data in this digital economy needs to be uncovered, protected, managed and maintained in order that the asset value can increase.

There lies one of the biggest barriers faced when it comes winning with data science as data governance is too often considered a road block that stifles innovation, restricts progress and consumes a large amount of the time we spend on data activities.

Research from IDC shows that data governance consumes approx. 81% of the time organizations spend on data activities, leaving only 19% of the time left to where really makes a difference – analysis and decision making – or taking action!

In order to free up time to spend taking action, we need to embrace data intelligence software that enables the workforce by supporting governance programs that provide access to the right (validated) data at the right time to the right (authorised) person.

A culture of data science

Data science can no longer be a specialist function owned by any one individual or small group. A reliance on “unicorn” employees is not only an inherent risk to the business due to dependency, but it also makes it impossible to scale and maximize the potential of data science across an organization.

In order to continue to grow as a key source of competitive advantage, we need to build teams in which diverse specialists pool their skills and knowledge, utilize intelligent data analytics tools and collaborate efficiency and seamlessly.

I like to think of Cyviz as being a people first company in a digital first world. My customers tell me that they care most about what our technology can do to support their people. When we design our work spaces we take time to understand the human interaction and workflow required to achieve our clients objectives.

In order to encourage users from different disciplines to come together, share information, ideas and to challenge the norm, the provisioning of an engaging, functional and easy to use space is critical to the overall success of the team.

Collaborative work environments (CWE’s) have been around for some time, they were all the rage in oil and gas some 10 years ago with the objective to unite People, Process, Tools and Technology with Facility. A cause that has never been so relevant as today as we look towards data science as a business enabler.

In many of our large enterprise customers today, I see increasing numbers of teams being formed with a mix of disciplines; engineers, business analysts, data scientists, operations and logistics all coming together. These teams need to be able to interact, communicate (sometimes across locations) and to show, share information and compare multiple data sets in order to achieve their objectives.

Who wouldn’t want to work in a room like this?

As much as collaboration itself is critical as it allows teams to learn from each other and take on larger problems than any individual can handle alone, an engaging work environment with the right tools and technologies to support the process of collaboration is of equal importance.

To break it down, an engaging, functional, physical space where users can Meet each other, Connect their devices, Visualize their data assets and Collaborate in order to take action can act as the foundation used to develop an organizational culture of data science.

Insight velocity

My old mentor, a strategic thinker and gospel of wisdom introduced me to the concept of “Insight Velocity” around ten years ago. Its principle is the speed at which you are able to take data and turn it into knowledge and decision making. So simple and pure and so relevant today as the data tsunami continues to engulf us.

In order to maximize insight velocity, data needs to be captured, processed using analytics software and then visualized in a meaningful manner that reduces the amount of time it takes users to understand the trends that the data is showing.

The visualization component is key as it enables users to gain the maximum insight in the shortest possible time frame. The challenge we see today is that there is a lack of understanding when it comes to providing visualization solutions, in the form of high-resolution display walls, that are fit for purpose and actually support users working with heavy data sets.

Let’s go back to oil and gas, where energy companies have been spending millions of dollars a year conducting seismic surveys in order to acquire huge volumes of data that allow their exploration teams to search for that holy grail of oil discovery. The most successful super majors such as Chevron, BP and Shell all realize that the visualization tools they provide for their team will directly affect their efficiency and productivity thus driving both top line revenue and bottom line earnings.

More pixels mean more information and the image above represents the portion of an oil reservoir you are able to see with a standard HD display. In this format it is difficult to get a real perspective of the make-up and structure of the reservoir, however if you were to view the same data set on a higher resolution display (with more pixels meaning more information can be displayed), the difference is significant:

The example above is as relevant today as it was 10 years ago and every organization serious about maximizing the potential of data science needs to provide their teams with the right tools that allow them to view their data without restriction.

Winning with data

Moving forward, the new digital economy will have winners and losers. The winning organizations will be those who are able to utilize the most data, to drive the maximum insight and make the most accurate decisions.

Data as an enabler 

As organizations look to tools such as AI and machine learning to automate much of the processing, sorting and governance of data, an opportunity exists for data science teams to spend more time focusing on problems that technology alone can’t solve.

Increase time spent on analysis and insight

The focus of these teams needs to be where they add maximum value – making data driven decisions and taking action. We need to decrease the amount of time we spend searching for and preparing data and increase the amount of time we spend analyzing data.

Build a culture of data science

We need to encourage a culture of data science across all functions of the organization, this means the formation of multi-disciplinary teams and the provision of engaging environments that support a collaborative work ethic.

Provide users with the right tools in the physical space

We need to equip the physical space with the right tools that allow them to share information and ideas easily and intuitively. Technology such as high-resolution displays that are fit for purpose and can help maximize efficiency and productivity through high performance visualization of data will also be fundamental.

Now back to my questions:

Are we spending enough time where it matters – taking action?

The answer here is probably not. Machine Learning and AI combined with data intelligence software has the potential to reverse the ratio completely so that our teams can spend 80% of their time collaborating and gaining insight into data.

Are we providing our teams with the right tools to be successful?

The answer again is probably not. We see the leaders in this space such as Accenture, EY and SAP taking a lead and starting to provide teams with an optimal environment that supports their data science efforts. However, many organizations and industries are late to the party and risk being left behind or innovating too slowly which can lead them own a dangerous path.

Data is the lifeblood of digital transformation and the winning organizations in the new world (digital) economy will be those that take the lead are able to spread the virus of data science throughout the company culture.

At Cyviz, we believe that great things happen when people meet and we understand that much of today’s meetings require teams to share their digital assets and to provide the tools that allow them to do so in the most intuitive and efficient manner.

I appreciate your time reading my blog and I welcome your feedback on the topics above, either drop me a message on LinkedIn or email me –


by Daniel Hanley, Vice President at Cyviz

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