Creative destruction: Using data skills to rethink business cultures

Data should underlie every business decision. Yet too often some very human cultural artifacts really lead the business down certain routes where it moves away from modelling decisions on the best data. It’s only human. Yet to be a better human guiding the corporate machine we need to transcend the way "we’ve always done it".

In the data age gut, tradition, and inertia shouldn’t be why strategic decisions are made. And yet, hand on heart, we all continue to do it, because that’s part of being human. Well, it’s time to be more than human, time to get creative. Time to smash the way "we’ve always done it." Time to apply some creative destruction to break down the worst parts of making decisions within organizations, and start using the data, technology, and creativity that lies around us, untapped.

The perception of data

It can be hard to for people to get a grip on what data is and means for them personally. "It’s just stuff," many might say.

Well, the last time the issue was looked at in depth (in the Alteryx 'Business Grammar’ research report) business leaders claimed that when it came to making decisions, 95 per cent of those surveyed used data and analytics to help drive change. A third considered data analytics an essential part of their decision-making in 2016. This figure was expected to rise, with 41 per cent expected to share this view by 2018.

Since the report aired the economy has entered a new phase of 'data for all'. Back in 2016 31 per cent of business leaders surveyed were already empowering business users with self-service analytics tools, putting the data in the hands of those line-of-business users that know their own data best.

This growing perception and use of data is a marked change. For decades business analytics was locked in to a cadre of skilled professionals performing very laborious statistical modelling -- and requiring an esoteric skill-set of mathematics, modelling, and programming.

A 'data democratization' is increasingly taking place though. Despite the high salaries of data scientists, data engineers, and data analysts -- these former 'high priests of data' no longer hold a monopoly on interacting with and understanding data. They are essential for complex projects, but no longer for all projects

My data, our data, not your data?

Within the enterprise, data of all types is being analyzed that would previously have gone to waste or been passively logged but never put to business use. Enterprises of all descriptions are discovering more about their potential customers, about how to respond to the changing market, about how sensor data from machinery or on livestock tells us how assets are performing.

Whatever type of data, whatever sector of the economy, enterprises are now, hopefully, consciously under the direction of a plan, making a change from being a passively 'knowing' culture to that of a learning culture, able to change behaviors to better meet predicted future demands.

Part of becoming a learning culture involves practicing on real data. Quite rightly, some are held back by (very valid) concerns about data privacy and security. That’s a fair point, but one that is managed by a practical approach that ensures any personal data shared with an organization is properly protected, and that business-value can still be derived -- respectfully.

Too often enterprises have gotten this wrong, with personal data leaking out from, or being used inappropriately by, the organization. Mistakes were made on many fronts -- and this was partly due to the plethora of tools and applications used: The analytics workflow was messy, error-prone, and confusing for any part-time experts to dabble in.

The market has matured a lot and of course legislation like the General Data Protection Regulation (GDPR) have underscored the seriousness of treating personal data with respect, privacy, and security. But what’s more there are solutions that enterprises (of whatever size) can turn to, to obviate the need for a whole tool chest of cobbled together data applications -- a data platform.

Putting in place a data platform allows an enterprise to control, secure, categorize, and even rate the data available. That way personal data is anonymized, secured, and removed from non-personal elements for making business decisions with. It means that older data, or faulty data can be downgraded, filed, or updated properly, without causing the same mistakes for the business users going back to ask new questions.

Rediscovering the data magic

If none of the above made any sense, or it’s too much too soon for our business, here are some stepping stones to rediscover the data magic that could be just a new way of working away.

• A data-led strategy: Set a goal from the top down and commit to it. Ensure that all department heads rely on data when setting their own strategies, and their teams all use data as evidence when reporting on how they will deliver on these goals.
• Setting up a data culture: Be an example, whether you are the CEO or a business executive -- be seen to be making careful, considered decisions that are based on data -- before you act. Link back successes and failures to the use of data. Always be honest and ensure that you follow best practices when it comes to keeping data secure and up to date. Put data metrics in people’s job descriptions.
• Making a high-performing data-oriented team: Even if you are a team of one, strive to improve by basing performance on data, experimenting based on data, and improving skills in data understanding. Strive for certification in the disciplines in which you operate and from the solutions providers you use.
• Setting best practices in data business decision-making: Continuously improve by keeping the organization at the forefront of emerging best practices. Learn at conferences. Partner with experts. Encourage personal growth from your business data users.

With these techniques in mind, be creative: Creatively destructive to old ways of working. Take out the old ways. Destroy complacency and bring in the rigors of a data-led approach. Hypothesize, test, report, and refine. Be creative in using data to be truthful and effective and better in all possible ways.

Photo Credit: Gorodenkoff/Shutterstock

Chiara Pensato is EMEA Director, Alteryx, and leads marketing for International regions, responsible for supporting exponential growth outside North America by generating demand and driving awareness, through all levers of the marketing mix. Prior to that she spent 15 years in the enterprise tech industry, moving from market leaders (IBM, Hewlett Packard, Vodafone), to IPO stage (Box, and now Alteryx) and start-ups (Topia). Over the years, she designed and ran multi-million marketing programs and enjoyed building teams from the ground up.