Why problem solving using analytics needs new thinking

For years, businesses have felt the pressure to ‘digitally transform’, and those pressures have only increased during this unprecedented time when so many companies are left with no choice but to move their operations to virtual environments. The novel coronavirus has forced many businesses to shift focus, reconsider their existing timelines and reevaluate the way they operate. The companies that have best endured this crisis are those that had prioritised the digitalisation of their business; in particular, those who had made investments in analytics and automation. Yet, AI still remains a grey area for many business leaders cross sector. How, as we shift towards a new normal, can we make data and analytics more accessible for problem solving?

To succeed, businesses must adapt their mindset. The narrative of reopening is too simplistic; rather, we must ‘reimagine’ business as the world around us shifts and changes. Only with fresh data-driven insights centred on operational efficiencies will businesses successfully reimagine what they do. They can forge a new and exciting path by embracing business insights fueled by data and driven by analytics.

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However, problem solving today demands a new culture and a new way of thinking to find these insights faster and more effectively, and it’s time for a bold and unifying software catalyst to displace the patchwork of existing data analytics solutions and dismantle the barriers between business teams.

In 1969, NASA put man on the moon using a slide rule, but we would be concerned if they did the same today. That’s because we shouldn’t do today’s work with the instruments of the past. Data processing and analytics are no different.

Process automation

The enterprise space has long been a hub of innovation. Around the world, intelligent, data-driven technologies now empower human decision making while liberating workers from the tedium of basic tasks. It is the ultimate synergy of human intuition and analytic insight. As more organisations evolve towards a technology and data-led culture, the rate at which smart systems can be scaled across all parts of a business has emerged as the true measurement of business success.

However, an information imbalance still exists for many businesses. As the amount of collected data explodes, the sheer quantity overwhelms the ability of legacy systems to process it and derive valuable output. Not only that, but employees don’t know how to use the data. Consequently, many organisations have little choice but to focus on narrow portions of data – an incomplete fraction when solutions demand greater percentage of the whole.

The emergent category of Analytic Process Automation, or APA, could be the key to capturing the best of man and machine at scale. APA automates business processes and grants even novice-level knowledge workers direct self-service access to business-critical data insights at speed. In practice, this means more employees can adopt — and benefit from — data with minimal training. This in turn dissipates the familiar tension between data specialists and business managers, where the latter have been reliant upon the former’s access to much-needed information. APA democratises data analytics in a way the business world hasn’t seen before.

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Just ask direct-to-consumer athletic wear retailer, Gymshark. They collect customer data from live events, social media engagement, and workout programmes on their app. Now by expanding access to this data, automating complex data processes, and broadening employees’ data skills across the business, the information is hard working for the company and giving a new visibility into how they are performing. This is empowering workers to take smarter decisions. For example, the business now uses data relating to customer spending, gender splits, and app engagement to intelligently choose locations for their popular pop-up retail events, using analytics to determine which city has more people who have made purchases within a certain radius – and thanks to an APA platform the data crunching is done in a fraction of the time.

Amid the Covid-19 crisis, fast analytics has allowed Gymshark to remain similarly nimble and efficient in their day-to-day decision-making as they shift emphasis to their online presence.

Critically, regular workers are harnessing these transformational insights, often using APA from home. In a world with only around two million data science PhDs, APA effectively upskills every worker into a data worker capable of solving business challenges and accelerating business outcomes that drive ROI.

Overdue evolution

There are parallels for this evolution. There was a time when building a website meant learning to write extensive lines of code. This eventually evolved to a partial self-service model via open-source software, and now the prevalence of simple drag-and-drop features allow anyone with an idea to create a personalised website.

As with the development of web design, APA platforms now allow users to get to the creative stage – or the ‘thinking stage’ – sooner. It leapfrogs the mundane tasks of sourcing, cleaning and organising data. The equivalent of web design’s user-friendly drag-and-drop features are the hundreds of building blocks that jump-start the process of creating useful analytic models.

Through a unified method of managing data analytics, automating business processes and elevating employees to spend their time on more strategic solving, APA reshapes the way companies generate data-driven insights and act on them. This enables upskilled employees in all parts of the business to ask hard questions and obtain swift answers without always relying upon the advanced skills of data experts.

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Prediction at speed

By replacing a range of cumbersome point solutions with one platform that sits across the entire analytic journey, APA also enables anyone in any organisation to build predictive models and use predictive data analytics to drive quick wins. Previously, data was reserved for machine learning specialists, but with the right, comprehensive system, we’re one step closer to closing the analytics skills gap. The more workers are empowered, the more AI becomes both explainable and repeatable.

Companies are currently using APA across industries for a multitude of time-sensitive purposes. Airlines use these platforms to hedge fuel, retailers to optimize hyperlocal merchandising and sports teams to do sentiment analysis.

In recent years, technology powerhouses have proven what can be achieved when data and analytics sit at the heart of a business model. It’s no surprise that the five most successful companies in the world are all data-driven, all fueled by a core focus on using data to understand, market to and increase revenue from their customers. This culture shift to democratise access to data and analytics across an organisation has enabled these companies to quickly capitalise on the data economy and accelerate digital transformation.

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Critical timing?

We’re at a crossroads when it comes to business strategy and how data is integrated. Today, the potential of businesses to solve is limited because only a small fraction of available organisational data is used. They key to overcoming the roadblock, ultimately, comes down to tackling the skills gap, and introducing platforms that are intuitive and capable of syncing with the existing workforce. It’s universally accepted that data adds value, but only when workers are able to pick out relevant, actionable insights.

The new APA category in analytics is helping businesses address this head on, offering precisely what they need to drive growth, empower staff and create time for creative problem solving. The key is simplicity.

Written by Shaan Mistry, senior product marketing manager – evangelism & enablement at Alteryx

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