No-code, self-service analytics are the shortcut to a data-driven business

Profile picture for user Francois Zimmerman By Francois Zimmerman October 21, 2021
Summary:
Data science is a high priority in the list of employability skills - and up-skilling employees will take time. Francois Zimmerman of Tableau shares some quick wins to achieve a data culture in business.

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The vast majority of businesses have already recognised that data is not just a by-product of their operations, but a resource that must be mined for actionable insights. Tapping into years of customer, supply chain and business operations data can uncover costly inefficiencies and opportunities to increase competitiveness.

However, many employees don’t have the right access, tools and skills to effectively use data to support their decisions. The more people that can make data-informed decisions across an entire business, the better outcomes organisations will drive. This means that building an internal data culture has become a top priority for businesses.

Data science has, therefore, been bumped right up to the top of the list of employable skills. Unfortunately, supply lags far behind demand. A recent Government report (Quantifying the UK Data Skills Gap 2021) found data skills have now become an essential entry requirement for two-thirds of UK occupations. But the report also found that in the last two years, almost half (46%) of UK businesses have struggled to recruit for roles that require data skills.

It identified 178,000 UK vacancies requiring ‘hard’ data skills – where work is centred around data and requires more advanced knowledge of it. However, little more than 10,000 data scientists graduate from UK universities per year. This means that businesses cannot rely only on incoming graduates to bridge the data skills gap. Instead, it requires significant investment in upskilling existing employees too. A Salesforce and IDC report found that by 2030, nine in 10 workers will need to learn new skills to do their jobs at a cost of £1.3 billion a year.

This upskilling will be critical to the future of business, but it will not be achieved overnight. And so, businesses are faced with a problem: how can they take steps towards creating a data culture and ultimately become a data-driven organisation more quickly?

Hyperautomation 

The answer lies in hyperautomation, and specifically low-code /no-code data and analytics platforms. These platforms enable more parts of the business to mine their data assets at scale and rapidly operationalise insights, predictions and recommendations by building ad-hoc dashboards and analytical toolkits that support every key business decision. Hyperautomation focuses on immediacy and context first and delivers rapid iterative gains to business users. The aim is to eliminate the data science bottleneck by enabling business domain experts to work with AI to answer important questions that the business is facing right now.

The quick wins in this space will be in augmented experiences rather than models which deliver fully automated workflow. Business users will need a variety of experiences that empower them to work alongside an AI to derive value from their data assets. The best tools can quickly and efficiently pull together large data sets from multiple sources into an analytics workbench that lets users understand “what has happened and why”, “what will happen”, and “what is likely to happen if a specific action is taken”.

This enables business users to sift through massive data sets and explore millions of combinations in seconds to automatically surface insights. If users can build custom predictions and perform what-if analysis, they can then bring these models and recommendations into their business workflow to operationalize the findings for the whole organization.

Here, hyperautomation can provide more transparency to ensure the ethical use of AI compared to very custom data science models. For example, a tool could ensure that bias detection is taken care of and provides insights into proxy variables and disparate impact so that you can remove distorting effects from your analysis.

Natural Language Processing (NLP) tools enable new experiences for business users by empowering them to ask ad-hoc questions of their data sets. This can power smart search functions that enable users to interact with data sets without writing strings of code, simply by typing queries into a search box. In the future, this will be one of the ways that we bridge the gap between analytics and collaboration platforms like Slack. Bringing data into every conversation will enable new data-driven ways of working.

Analytics

Augmented analytics experiences enable more business users to work with advanced statistical models so that they can discover deeper insights behind unusual data points. Some tools are capable of spotting outliers in charts and can sift through the data behind points of interest to automatically generate explanations. This enables deeper understanding and drives up data literacy for more users.

A drawback with some no-code analytics is that the models’ actual operations can lie within an even more opaque box than usual. There may be little to no explanation as to why the analytics tools spit out the answers and insights that they do, with reasoning lost in the layers of abstraction. An ideal tool visualises insights to aid the user’s understanding, particularly when the insights are forecasts based on predictive analytics. Graphs and maps that show how situations may progress can be incredibly useful methods to build understanding as to the ‘why’ and put insights into context. Data cannot make difficult decisions entirely on behalf of humans, but it can empower humans to make the best ones themselves. Human understanding is critical to a data driven organisation, to ensure accountability is not lost.

Low-code and no-code analytics tools that do not require coding fluency to operate are not new, but are on a meteoric rise, with Gartner forecasting that the global low-code development technologies market will grow 23% in 2021 alone. The growing need for hyperautomation in businesses will be one of the top three drivers for this low-code adoption. This in turn has been driven not only by the data skills gap, but by pandemic-related factors such as increased home-working.

With many people continuing to work outside the office, it has become all the more important to rely on cloud-based software that enables remote collaboration. This provides the perfect environment for hyperautomation tools, as disparate data sources can be connected in the cloud and real-time analytics layered on top to provide valuable insights. And now, with accessible no-code tools that tap into these capabilities, business users - even those with limited data skills - can be empowered to make data-driven decisions from anywhere.